Patents by Inventor Amir H. Hormati
Amir H. Hormati has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11948159Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scalable matrix factorization. A method includes obtaining a Structured Query Language (SQL) query to create a matrix factorization model based on a set of training data, generating SQL sub-queries that don't include non-scalable functions, obtaining the set of training data, and generating a matrix factorization model based on the set of training data and the SQL sub-queries that don't include non-scalable functions.Type: GrantFiled: April 8, 2020Date of Patent: April 2, 2024Assignee: Google LLCInventors: Amir H. Hormati, Lisa Yin, Umar Ali Syed, Mingge Deng
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Patent number: 11928559Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for transformation for machine learning pre-processing. In some implementations, an instruction to create a model is obtained. A determination is made whether the instruction specifies a transform. In response to determining that the instruction specifies a transform, a determination is made as to whether the transform requires statistics on the training data. The training data is accessed. In response to determining that the transform requires statistics on the training data, transformed training data is generated from both the training data and the statistics. A model is generated with the transformed training data. A representation of the transform and the statistics is stored as metadata for the model.Type: GrantFiled: April 8, 2020Date of Patent: March 12, 2024Assignee: Google LLCInventors: Jiaxun Wu, Amir H. Hormati
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Patent number: 11842291Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.Type: GrantFiled: December 6, 2022Date of Patent: December 12, 2023Assignee: Google LLCInventors: Mingge Deng, Amir H. Hormati, Xi Cheng
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Publication number: 20230297583Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data. Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data. The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.Type: ApplicationFiled: May 25, 2023Publication date: September 21, 2023Applicant: Google LLCInventors: Xi Cheng, Amir H. Hormati, Lisa Yin, Umar Syed
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Patent number: 11693867Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data. The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.Type: GrantFiled: August 6, 2020Date of Patent: July 4, 2023Assignee: Google LLCInventors: Xi Cheng, Amir H. Hormati, Lisa Yin, Umar Syed
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Publication number: 20230094005Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.Type: ApplicationFiled: December 6, 2022Publication date: March 30, 2023Applicant: Google LLCInventors: Mingge Deng, Amir H. Hormati, Xi Cheng
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Patent number: 11544596Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.Type: GrantFiled: April 8, 2020Date of Patent: January 3, 2023Assignee: Google LLCInventors: Mingge Deng, Amir H. Hormati, Xi Cheng
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Publication number: 20220405623Abstract: The disclosure is directed to a query-driven machine learning platform for generating feature attributions and other data for interpreting the relationship between inputs and outputs of a machine learning model. The platform can receive query statements for selecting data, training a machine learning model, and generating model explanation data for the model. The platform can distribute processing for generating the model explanation data to scale in response to requests to process selected data, including multiple records with a variety of different feature values. The interface between a user device and the machine learning platform can streamline deployment of different model explainability approaches across a variety of different machine learning models.Type: ApplicationFiled: June 22, 2021Publication date: December 22, 2022Inventors: Xi Cheng, Lisa Yin, Jiashang Liu, Amir H. Hormati, Mingge Deng, Christopher Avery Meyers
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Publication number: 20210357402Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.Type: ApplicationFiled: August 6, 2020Publication date: November 18, 2021Applicant: Google LLCInventors: Xi Cheng, Amir H. Hormati, Lisa Yin, Umar Syed
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Publication number: 20200320413Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.Type: ApplicationFiled: April 8, 2020Publication date: October 8, 2020Inventors: Mingge Deng, Amir H. Hormati, Xi Cheng
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Publication number: 20200320072Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scalable matrix factorization. A method includes obtaining a Structured Query Language (SQL) query to create a matrix factorization model based on a set of training data, generating SQL sub-queries that don't include non-scalable functions, obtaining the set of training data, and generating a matrix factorization model based on the set of training data and the SQL sub-queries that don't include non-scalable functions.Type: ApplicationFiled: April 8, 2020Publication date: October 8, 2020Inventors: Amir H. Hormati, Lisa Yin, Umar Ali Syed, Mingge Deng
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Publication number: 20200320436Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for transformation for machine learning pre-processing. In some implementations, an instruction to create a model is obtained. A determination is made whether the instruction specifies a transform. In response to determining that the instruction specifies a transform, a determination is made as to whether the transform requires statistics on the training data. The training data is accessed. In response to determining that the transform requires statistics on the training data, transformed training data is generated from both the training data and the statistics. A model is generated with the transformed training data. A representation of the transform and the statistics is stored as metadata for the model.Type: ApplicationFiled: April 8, 2020Publication date: October 8, 2020Inventors: Jiaxun Wu, Amir H. Hormati